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This is a Quantitative Study study.

Computer Vision-Enhanced Smart Manufacturing for Continuous Glucose Monitoring Device Production

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Automated quality control improves defect detection in glucose monitoring devices, highlighting efficiency gains.

Automated quality control improves the detection of defects, reducing errors in glucose measurement.
Integration of computer vision and deep learning models, such as YOLOv4, enhances production accuracy.
Real-time monitoring identifies manufacturing anomalies, supporting immediate corrective action.
The approach ensures compliance with FDA and ISO standards, boosting product reliability and efficiency.

K. Nakkeeran (2025) studied this question.

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